{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n",
    "#print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\deeplearn\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1. 对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。\n",
    "# 连续型特征即数值型特征，可以用直方图进行可视化其分布，比如seaborn的displot方法可以对数值型特征绘制直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(train[\"cnt\"], bins = 50, kde=True)\n",
    "plt.xlabel(\"daily times of bikesharing\", fontsize=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2368cc2b3c8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。 \n",
    "plt.scatter(train[\"temp\"], train[\"atemp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。\n",
    "1、特征之间高度相关，可考虑进行PCA降维（特征层面）或加正则项（模型层面）\n",
    "2、两个特征的相关性特别强，比如绝对值高于0.9，则可以考虑只保留其中一个特征以达到降维的目的。\n",
    "3、在选用模型时加入L1正则项以在训练模型的时候自动淘汰一些特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。\n",
    "# 课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 ，并重新训练最小二乘线性回归、岭回归、和Lasso模型。\n",
    "见文件Regression_BikeSharing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5. 代码中给出了岭回归（RidgeCV）和Lasso（LassoCV）超参数（alpha_）调优的过程，\n",
    "# 请结合两个最佳模型以及最小二乘线性回归模型的结果，给出什么场合应该用岭回归，什么场合用Lasso，什么场合用最小二乘。\n",
    "最小二乘： 适用与特征之间相关性不强的数据\n",
    "岭回归： 适用与特征之间有一些相关性的数据\n",
    "Lasso回归： 适用与特征之前有很多强相关性的数据\n",
    "最小二乘线性回归中，目标函数只考虑了模型对训练样本的拟合程度，容易过拟合。\n",
    "岭回归使得线性回归系数收缩，模型稳定。当输入特征之间存在共线性时使用较好。\n",
    "lasso也会收缩回归系数。当正则参数取合适值时，使得有些线性回归系数为0，得到稀疏模型。\n",
    "当输入特征多，有些特征与目标变量之间相关性很弱时，可能只选择强相关的特征，模型解释性好。 \n",
    "通过模型结果来看，不同模型中各个变量的比重不同。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
